AI-Based Tool Improves Diagnosis of Breast Cancer Tumors and Ability to Predict Risk of Recurrence

The greater diagnostic precision enabled by the AI-based tool developed by researchers at the Karolinska Institutet (Stockholm, Sweden) can lead to more personalized treatment for the large group of breast cancer patients with intermediate risk tumors.

Every year, around two million women globally develop breast cancer. In the diagnostic procedure, tissue samples of the tumor are analyzed and graded by a pathologist and categorized by risk as low (grade 1), medium (grade 2) or high (grade 3). This helps the doctor determine the most suitable treatment for the patient. Hospitals have recently started to make limited use of molecular diagnostics to improve the precision of breast cancer risk assessment, but these methods are often costly and time-consuming.

In a study based on an extensive microscopic image bank of 2,800 tumors, researchers trained a new AI-based method for tissue analysis to recognize characteristics of high-resolution microscopic images from patients classified with grade 1 and grade 3 tumors. In an evaluation of the AI model, the researchers found that their AI-based method can further divide the patients with grade 2 tumors into two sub-groups, one high-risk and one low-risk that are clearly distinguishable in terms of the recurrence risk. The method is not yet ready for clinical application, but a regulatory approved product is under development. The researchers will now be further evaluating the method with the aim to have a product out on the market by 2022.

“Roughly half of breast cancer patients have a grade 2 tumor, which unfortunately gives no clear guidance on how the patient is to be treated,” said the study’s first author Yinxi Wang, doctoral student at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet. “Consequently, some of the patients are over-treated with chemotherapy while others risk being under-treated. It’s this problem that we’ve tried to resolve.”

“One big advantage of the method is that it’s cost-effective and fast, since it’s based on microscope images of dyed tissue samples, which is already part of hospital procedure,” said co-last author Johan Hartman, professor of pathology at the Department of Oncology-Pathology, Karolinska Institutet, and pathologist at the Karolinska University Hospital. “It enables us to offer this type of diagnosis to more people and improves our ability to give the right treatment to any one patient.”

“It’s fantastic that deep learning can help us develop models that don’t just reproduce what specialist doctors do today, but also enable us to extract information beyond the scope of the human eye,” added co-last author Mattias Rantalainen, associate professor and research group leader at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet.

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